Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients

Abstract

Purpose of review Artificial intelligence (AI) is a transformative technology that has the potential to improve and augment the clinical workflow in supportive and palliative care (SPC). The objective of this study was to provide an overview of the recent studies applying AI to SPC in cancer patients. Recent findings Between 2020 and 2022, 29 relevant studies were identified and categorized into two applications: predictive modeling and text screening. Predictive modeling uses machine learning and/or deep learning algorithms to make predictions regarding clinical outcomes. Most studies focused on predicting short-term mortality risk or survival within 6 months, while others used models to predict complications in patients receiving treatment and forecast the need for SPC services. Text screening typically uses natural language processing (NLP) to identify specific keywords, phrases, or documents from patient notes. Various applications of NLP were found, including the classification of symptom severity, identifying patients without documentation related to advance care planning, and monitoring online support group chat data. Summary This literature review indicates that AI tools can be used to support SPC clinicians in decision-making and reduce manual workload, leading to potentially improved care and outcomes for cancer patients. Emerging data from prospective studies supports the clinical benefit of these tools; however, more rigorous clinical validation is required before AI is routinely adopted in the SPC clinical workflow.

Ethical issues in pain and palliation

“…Most artificial intelligence and machine learning research in palliative care is concentrated on predicting mortality and prognosis [ 7 , 8 ] and on the assessment of psychological symptoms and distress [ 9 ]. Moreover, artificial intelligence and machine learning techniques have been employed for pain diagnosis in the field of automatic pain assessment that relies on objective methods such as different behaviors and biosignals to assess pain and pain-related features [ 10 ].…”

Applications of the Natural Language Processing Tool ChatGPT in Clinical Practice: Comparative Study and Augmented Systematic Review

“…In clinical practice, NLP can facilitate various tasks, such as disease diagnosis, treatment decision support, automation of clinical tasks, and data mining [ 6 ]. For instance, NLP algorithms have been used to screen and identify patients at risk for specific conditions [ 7 ], aid in the diagnosis of diseases by analyzing electronic health records (EHRs) [original: Demner-Fushman, D., & Chapman, W. W. (2017)] (new [ 8 ]) , provide decision support in treatment planning [original: Wang, Y et al (2017)] (new [ 9 ]) , and automate routine clinical tasks [original: Devlin, J. et al (2019)] (new [ 10 ]) . Furthermore, NLP has been employed in the analysis of large-scale medical literature to identify trends, generate hypotheses, and inform clinical decision-making [original: Brown, T. B. et al (2020)] (new [ 11 ]) .…”